---
title: "Coronavirus COVID-19 Brasil"
output: 
  flexdashboard::flex_dashboard:
    source_code: embed
    css: styles.css
    vertical_layout: scroll
    orientation: rows
    navbar:
    - { title: "MAVE", href: "http://covid-19.procc.fiocruz.br/" }
editor_options: 
  chunk_output_type: console
---

```{r, echo=FALSE, results='hide'}
library(tidyverse)
library(flexdashboard)
library(leaflet)
library(lubridate)
library(DT)
library(gridExtra)
library(brazilmaps)
library(leafpop)
library(sf)
```


```{r, echo=FALSE, results='hide', cache=FALSE}
###############################################
# Script feito por Leo Bastos (@leosbastos)
# 
# Esse (flex)dashboard foi desenvolvido por Leo Bastos 
# (pesquisador do MAVE/PROCC/Fiocruz) e por Lucas Bianchi 
# (doutorando em epidemiologia, ENSP/FIocruz). 
# Adaptado do dashboard desenvolvido pela professora 
# Paula Moraga (Univeristy of Bath, UK) usando dados coletados 
# pelo agregador desenvolvido por Wesley Cota 
# (doutorando da UFV e Universidad de Zaragoza, Espanha).


# Lendo dados

d.cidades <- read.csv("https://raw.githubusercontent.com/wcota/covid19br/master/cases-brazil-cities-time.csv",
                       sep=",",encoding = "UTF-8", 
                       stringsAsFactors = F) %>% 
  filter(!state == "TOTAL") %>% 
  mutate(status = "Confirmado",
         date = ymd(date)) 


d.cidades.cum <- read.csv("https://raw.githubusercontent.com/wcota/covid19br/master/cases-brazil-cities.csv",
                       sep=",",encoding = "UTF-8", 
                       stringsAsFactors = F) 


d.estados <- read.csv("https://raw.githubusercontent.com/wcota/covid19br/master/cases-brazil-states.csv", 
  sep=",", encoding = "UTF-8", stringsAsFactors = F) %>% 
  mutate(date = ymd(date))


## Mapas do Brasil pelo pacote brazilmaps

# Por municipio
cities_map <- get_brmap(geo = "City",
                        #geo.filter = list(Region = 5),
                        class = "sf")

# Juntando os casos por cidade e mapa
cities_map <- cities_map %>% 
  left_join(d.cidades.cum, by = c("City" = "ibgeID")) 

 
# Por estado
state_map <- get_brmap(geo = "State",
                        #geo.filter = list(Region = 5),
                        class = "sf")

```




```{r, echo=FALSE, results='hide'}

# leaflet por municipio
cities_map <- cities_map %>% 
  mutate(
    totalCases = replace_na(totalCases, replace = 0),
    deaths = replace_na(deaths, replace = 0),
    cases.cat = cut(totalCases, 
                     breaks = c(-1, 0, 10, 100, 500, 1000, 1000000),
                     labels = c("Nenhum caso", "[1 - 10]", "[11-100]", "[101 - 500]", "[501 - 1000]", "Acima de 1000"), 
                    right = T
    )
  )
pal.cities <- colorFactor(palette = "YlOrRd", cities_map$cases.cat)

# Criando o mapa por município
l <- leaflet( cities_map ) %>%
  addTiles() %>%
  addProviderTiles(providers$CartoDB.Positron, group = "Claro") %>%
  addProviderTiles(providers$HERE.satelliteDay, group = "Satélite") %>%
  addPolygons(color = "#444444", weight = 1, smoothFactor = 0.5,
    opacity = 1.0, fillOpacity = 0.5,
    fillColor = ~pal.cities(cases.cat),
    highlightOptions = highlightOptions(color = "white", weight = 2,
      bringToFront = TRUE, ),
    popup = ~paste0("", nome,":
", 
                             "
                               Confirmados:
                             ", 
                             totalCases,
                             "
",
                             "
                               Óbitos:
                             ", 
                             deaths,
                             "")
    ) %>%  
    addLegend("bottomright", 
            pal = pal.cities,
            values = ~cases.cat,
            title = "Casos confirmados do COVID-19",
            opacity = 1
  ) %>% 
  addLayersControl(
    baseGroups = c("Claro", "Satélite"),
    options = layersControlOptions(collapsed = TRUE)
  )
```



```{r, echo=FALSE, results='hide'}

# leaflet por estado
d.estados.cum <- d.estados %>% filter(state != "TOTAL") %>% 
  group_by(state) %>% 
  summarise(
    totalCases = max(totalCases),
    deaths = max(deaths)
  )

state.cod <- d.cidades %>% 
  mutate(
    state_cod = as.numeric(substr(x = as.character(ibgeID),start = 1, stop = 2))
  ) %>% 
  group_by( state, state_cod ) %>% 
  summarise(
    n = n()
  ) %>% select(., -n)


state_map <- state_map %>% 
  left_join( state.cod, by = c("State"="state_cod")) %>% 
  left_join( d.estados.cum, by = "state" )

state_map <- state_map %>% 
  mutate(
    totalCases = replace_na(totalCases, replace = 0),
    deaths = replace_na(deaths, replace = 0),
    cases.cat = cut(totalCases, 
                     breaks = c(-1, 0, 100, 500, 1000, 5000, 1000000),
                     labels = c("Nenhum caso", "[1 - 100]", "[101-500]", "[501 - 1000]", "[1001 - 5000]", "Acima de 5000"), 
                    right = T
    )
    ) # state_map %>% sf::st_drop_geometry() %>% View()
pal.state <- colorFactor(palette = "YlOrRd", domain = state_map$cases.cat)

# Criando o mapa por município
l2 <- leaflet( state_map ) %>%
  addTiles() %>%
  #addProviderTiles(providers$CartoDB.DarkMatter, group = "Escuro") %>%
  addProviderTiles(providers$CartoDB.Positron, group = "Claro") %>%
  addProviderTiles(providers$HERE.satelliteDay, group = "Satélite") %>%
  addPolygons(color = "#444444", weight = 1, smoothFactor = 0.5,
    opacity = 1.0, fillOpacity = 0.5,
    fillColor = ~pal.state(cases.cat),
    highlightOptions = highlightOptions(color = "white", weight = 2,
      bringToFront = TRUE, ),
    popup = ~paste0("", nome,":
", 
                             "
                               Confirmados:
                             ", 
                             totalCases,
                             "
",
                             "
                               Óbitos:
                             ", 
                             deaths,
                             "")
    ) %>%
  addLegend("bottomright", 
            pal = pal.state,
            values = ~cases.cat,
            title = "Casos confirmados do COVID-19",
    #labFormat = labelFormat(prefix = "$"),
    opacity = 1
  ) %>% 
  addLayersControl(
    baseGroups = c("Claro", "Satélite"),
    options = layersControlOptions(collapsed = TRUE)
  )

```



```{r echo=FALSE, results='hide'}

tbl.covid.state <- d.estados %>% 
  filter(state != "TOTAL" & totalCases >= 100) %>% 
  mutate(
    date = ymd(date)
  )

g.state <- tbl.covid.state %>%  
  ggplot(aes( y = totalCases, x = date, group = state, color = state )) + 
  geom_point(show.legend = F) +
  scale_color_discrete("Estados") + 
  geom_line(show.legend = F) + 
  geom_label( data = tbl.covid.state %>% 
                filter(date == max(date)), 
              mapping = aes(x = date, y = totalCases, label = state ), 
              show.legend = F ) +
  scale_y_continuous(trans = "log10") +
  ylab("Total de casos de COVID-19") +
  #xlab("Dia") +
  scale_x_date(name = "Dia", date_breaks = "5 days", date_labels = "%d/%m") +
  theme_bw(base_size = 14) + 
  ggtitle("Estados com transmissão comunitária")


g.state.obitos <- tbl.covid.state %>% 
  filter(deaths > 10) %>% 
  ggplot(aes( y = deaths, x = date, group = state, color = state )) + 
  geom_point(show.legend = F) +
  geom_line(show.legend = F) + 
  geom_label( data = tbl.covid.state %>% 
                filter(deaths > 10) %>%  
                filter(date == max(date)), 
              mapping = aes(x = date, y = deaths, label = state ), 
              show.legend = F ) +
  scale_y_continuous(trans = "log10") +
  ylab("Óbitos por COVID-19") +
  #xlab("Dia") +
  scale_x_date(name = "Dia", date_breaks = "1 day", date_labels = "%d/%m") +
  theme_bw(base_size = 14) + 
  ggtitle("Óbitos em estados com transmissão comunitária", subtitle = "(pelo menos 10 óbitos notificados)" )


g.state.newxtot <- tbl.covid.state %>% 
    ggplot(aes( y = newCases, x = totalCases, color = state, group = state)) + 
    geom_line( alpha = 0.5, show.legend = F) +
    geom_label( data = tbl.covid.state %>% 
                filter(date == max(date)), 
                mapping = aes(newCases, x = totalCases, label = state ), 
                show.legend = F ) +
    scale_x_continuous(trans = "log10") +
    scale_y_continuous(trans = "log10") +
    xlab("Total de casos") +
    ylab("Casos novos") +
    theme_bw(base_size = 14)

tbl.covid.state <- tbl.covid.state %>% 
  group_by(state) %>% 
  mutate(
    tempo = as.numeric( date - min(date) ),
    maxtempo = max(tempo)
  ) %>% ungroup()

g.cases.100 <- tbl.covid.state%>% 
  ggplot(aes( y = totalCases, x = tempo, group = state, color = state)) + 
  geom_point(show.legend = F) +
  geom_line(show.legend = F) + 
  geom_label( data = tbl.covid.state %>% 
                filter(date == max(date) ), 
              mapping = aes(x = maxtempo, y = totalCases, label = state ), 
              show.legend = F ) +
  scale_y_continuous(trans = "log10") +
  ylab("Total de casos") +
  xlab("Dias a partir do centésimo caso") +
  theme_bw(base_size = 14) 


g.obitos.100 <- tbl.covid.state %>% 
                filter(deaths > 10 ) %>% 
  ggplot(aes( y = deaths, x = tempo, group = state, color = state)) + 
  geom_point(show.legend = F) +
  geom_line(show.legend = F) + 
  geom_label( data = tbl.covid.state %>% 
                filter(deaths > 10 ) %>% 
                filter(date == max(date) ), 
              mapping = aes(x = maxtempo, y = deaths, label = state ), 
              show.legend = F ) +
  scale_y_continuous(trans = "log10") +
  ylab("Total de óbitos") +
  xlab("Dias a partir do centésimo caso e 10o óbito") +
  theme_bw(base_size = 14) 
```








COVID-19 por estados
=====================================  


Row
-----------------------------------------------------------------------

### Casos confirmados de COVID-19

```{r}
#usar o dconfirmed pq o dados remove
valueBox(value = sum(d.estados.cum$totalCases),
         color = "orange",
         #icon = "glyphicon-ok"
         )
```


### Óbitos por COVID-19

```{r}
#usar o dconfirmed pq o dados remove
valueBox(value = sum(d.estados.cum$deaths),
         color = "red",
         #icon = "glyphicon-ok-sign"
         )
```

### Letalidade

```{r}
#usar o dconfirmed pq o dados remove
valueBox(value = paste( round(sum(d.estados.cum$deaths) / sum(d.estados.cum$totalCases) * 100), "%"),
         color = "red",
         #icon = "glyphicon-ok-sign"
         )
```



Row 
-----------------------------------------------------------------------

### Casos confirmados por estado

```{r}
g.state
```

### Clique nos estados para ver as estatísticas {data-width=500}


```{r}
l2
```


Row 
-----------------------------------------------------------------------

### Óbitos por estado

```{r}
g.state.obitos
```

### Óbitos confirmados a partir do 100o dia

```{r}
g.obitos.100
```









COVID-19 por municípios
===================================== 


Row
-----------------------------------------------------------------------

### Casos confirmados de COVID-19

```{r}
#usar o dconfirmed pq o dados remove
valueBox(value = sum(d.estados.cum$totalCases),
         color = "orange",
         #icon = "glyphicon-ok"
         )
```


### Óbitos por COVID-19

```{r}
#usar o dconfirmed pq o dados remove
valueBox(value = sum(d.estados.cum$deaths),
         color = "red",
         #icon = "glyphicon-ok-sign"
         )
```

### Letalidade

```{r}
#usar o dconfirmed pq o dados remove
valueBox(value = paste( round(sum(d.estados.cum$deaths) / sum(d.estados.cum$totalCases) * 100), "%"),
         color = "red",
         #icon = "glyphicon-ok-sign"
         )
```



Row 
-----------------------------------------------------------------------

### Casos confirmados por estado

```{r}
g.cases.100
```

### Clique nos municípios para ver as estatísticas {data-width=500}


```{r}
l
```





Informação atualizada em `r format(max(d.cidades$date), "%d/%m/%Y")` 
===================================== 

Fonte dos dados

**Casos de Covid-19 no Brasil**

https://github.com/wcota/covid19br/


Software

**The R Project for Statistical Computing**

https://www.r-project.org/


DT: https://rstudio.github.io/DT/  
flexdashboard: https://rmarkdown.rstudio.com/flexdashboard/    
ggplot2: https://ggplot2.tidyverse.org/  
leaflet: https://rstudio.github.io/leaflet/  
leafpop: https://github.com/r-spatial/leafpop
brazilmaps: http://github.com/rpradosiqueira/brazilmaps




Dashboard desenvolvido por Leonardo Bastos (pesquisador do MAVE/PROCC/Fiocruz) e por [Lucas Bianchi](http://www.lucasbianchi.com/) (doutorando em epidemiologia, ENSP/FIocruz). Adaptado do dashboard desenvolvido pela professora [Paula Moraga](http://www.paulamoraga.com/) (Univeristy of Bath, UK) usando dados coletados pelo agregador desenvolvido por [Wesley Cota](https://wesleycota.com/) (doutorando da UFV e Universidad de Zaragoza, Espanha).